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Document last updated: 2025-04-09


1 Methods

1.1 DNA extraction and amplicon sequencing

We extracted and sequenced DNA from a total of 245 samples, comprised of 5 random stool samples from before the experimental diets began (referred to as Day 0 or Week 0), while all mice were fed the their standard “Control-diet”, and then weekly collections of 5 random stool samples per cohort over the next 12 weeks (5 replicates x 4 cohorts x 12 weeks). DNA extractions were performed using []. Taxonomic profiling was performed by sequencing bacterial 16S rRNA genes. The V3-V4 region of bacterial (and archaeal) 16S rRNA genes was amplified using primers 515f-R806 (Bates et al., 2010). PCR amplifications were performed using previously described methods (Mueller et al., 2016). In the first PCR, sample barcoding was performed with forward and reverse primers each containing a 6-bp barcode; 22 cycles with an annealing temperature of 60oC were performed. The second PCR added Illumina adaptors over 10 cycles with an annealing temperature of 65°C. Amplicon clean-up was performed with a 0.9 ratio of AMPure XP beads (Beckman Coulter, Indianapolis IN), following manufacturer’s instructions and final elutions were performed with 30µl Elution Buffer. Following clean-up, samples were quantified with an Invitrogen Quant-iTTM ds DNA Assay Kit on a BioTek Synergy HI Hybrid Reader and pooled at a concentration of 10 ng per sample. A final clean-up step was performed on pooled samples using a 0.9 ratio of AMPure XP beads. Samples were sequenced on an Illumina MiSeq platform with PE250 chemistry at Los Alamos National Laboratory. Unprocessed sequences are available through NCBI’s Sequence Read Archive ().

1.2 Microbial community sequence analysis

Bacterial sequences were processed using Usearch11 (Edgar, 2010). Samples were demultiplexed, paired ends merged, quality filtered and globally trimmed using a fastq_maxee threshold of 1.0 (Edgar and Flyvbjerg, 2015), dereplicated, and singletons were removed. Chimeras were removed and 97% OTU clustering was performed independently for the two datasets with the -cluster_otus command using the UPARSE-OTU algorithm (Edgar, 2013). Previous analyses have shown congruent ecological patterns with use of OTUs versus exact sequence variants (ESVs) for delineating microbial taxa (92). OTU tables were created using the -otutab command. Bacterial OTUs were classified using the Ribosomal Database Project (RDP) classifier v.19 (Wang et al., 2007). Next-generation sequencing of 16S rRNA genes resulted in 5,043,233 reads (average of 20,585 ± 3,467 (SD) reads per sample, n = 245 samples). These reads yielded 1,090 OTUs. Domain-level analyses revealed that 99.99% of reads were classified as “Bacteria”, 0.003% as “Eukaryota”, and 0.01% were unclassified at the domain level. The dataset was then filtered to exclude all domains except Bacteria, all reads assigned at >= 80% confidence at the phylum-level (n = 12,991), reads assigned to the class Chloroplast (n = 122), and the remaining singleton reads (n = 32). We rarefied via subsampling without replacement to 13,006 sequences per sample to account for uneven sequencing depth and from that, 624 bacterial OTUs (97% sequence similarity) were identified from 243 samples.

Phylum/Class/Order/Family/Genus-level bar plots of the community profiles by week along the 12 week longitudinal can be found in SI Figures XYZ (or here: Taxonomy.

1.3 Statistical analysis

Microbial community analyses were conducted primarily in the vegan (Oksanen et al., 2018) and phyloseq (McMurdie and Holmes, 2013) packages in the R programming environment unless otherwise noted. Patterns in microbial community composition were visualized using non-metric multidimensional scaling (NMDS) using Bray-Curtis (abundance-weighted) and Jaccard (binary presence/absence) distance metrics.

We investigated the degree to which differences in microbial community profiles were explained by experimental factors ….

We also examined differences across initial …

1.3.1 Week-0 baseline characterization

To quantify inter-individual variability prior to dietary intervention, we analyzed five stool samples collected at baseline (Week-0), before diet assignment. Alpha diversity was measured using both observed OTU richness and Shannon entropy. Taxonomic profiles were summarized at the family level using relative abundance from a transformed OTU table. Pairwise Bray–Curtis dissimilarities were computed to assess baseline community divergence. Additionally, we calculated distances to the group centroid using PERMDISP to quantify β-dispersion. These metrics were used to establish the null range of baseline variation, providing an ecological benchmark against which subsequent compositional shifts were interpreted.

1.3.2 Multi-site (sample) dissimilarity partitioning

Using the R package betapart (v1.6), we computed the 3 abundance-based multiple-site dissimilarities (balanced variation fraction,abundance-gradient fraction, and overall dissimilarity) using the Bray-Curtis family of dissimilarity indices, in addition to the corresponding presence/absence-based multiple-site dissimilarities accounting for the spatial turnover and the nestedness components of beta diversity, and the sum of both values using beta.multi(index.family = sorensen”). To compute abundance-based beta diversity, we used unrarefied OTU tables to retain quantitative abundance information. Rarefaction can eliminate natural abundance gradients, resulting in inflated balanced dissimilarity and zero-valued gradient components in Bray-Curtis partitioning (Baselga 2017). We therefore retained untransformed counts for βBRAY partitioning, and only applied rarefied tables for presence/absence-based analyses (e.g., βSOR).

The statistical significance of these explanatory experimental factors was assessed using adonis2(), a function based on permANOVA, within the vegan R-package (McArdle and Anderson, 2001). Adonis is a permutational (n = 999) multivariate analysis of variance test that partitions our Bray-Curtis distance matrices among sources of variation (Anderson, 2001).

The distinctness of the … communities was assessed using the Random Forests classification algorithm (Breiman, 2001), using 1000 trees. As implemented in the R package ‘randomForest’, the algorithm constructs each tree using a different bootstrap sample from the original data (approximately 1/3 of the cases are left out of the bootstrap sample and not used in the construction of the kth tree), thus providing an unbiased estimate of the test set error without the need for separate cross-validation test.

2 Introduction

[note:I know intros don’t use subheaders, these are just for organization]

Microbiota–Diet Interactions in NAFLD Pathogenesis Non-alcoholic fatty liver disease (NAFLD) is a leading cause of chronic liver disease globally, strongly associated with dietary patterns, particularly excessive intake of high-fat diets (HFDs). Numerous clinical and preclinical studies have identified shifts in gut microbiota composition during NAFLD development, implicating the microbiome as a mediator of metabolic dysregulation, hepatic steatosis, and inflammation. Experimental evidence supports roles for microbially derived metabolites—including short-chain fatty acids, secondary bile acids, and endotoxins—in promoting liver fat accumulation, disrupting immune signaling, and modulating systemic insulin sensitivity.

Microbia contribute fundamentally to host metabolism, immune development, and ecological resilience. [A substantial body of work demonstrates that species richness (α-diversity) influences these outcomes, yet variation or dissimilarity between communities (β-diversity)… - might reposition this part]

In an effort to delineate the microbial contributions to NAFLD, many(?) studies have sought to identify specific taxa associated with disease severity or progression. However, this taxon-centric approach faces several inherent limitations. First, the gut microbiome functions as a complex, interdependent community, where species do not act in isolation but instead participate in collective metabolic networks and ecological interactions. Second, taxonomic resolution in amplicon-based profiling is often insufficient to capture strain-level variation that may drive host outcomes. Finally, inter-individual variability and high-dimensional sparsity limit the reproducibility and interpretability of taxon-level associations, particularly in small or heterogeneous cohorts.

As a result, relatively little is known about the community-level principles that govern microbiota restructuring during chronic dietary perturbation. In particular, the ecological processes underlying microbial community divergence across diets and over time remain poorly defined. Without this understanding, it is difficult to distinguish between deterministic patterns of microbial selection (e.g., driven by nutrient availability or host immune tone) and stochastic community drift. This gap limits our ability to generalize microbial signatures of disease or therapeutic response across models and populations.

Community Dissimilarity as a [Mechanistic] Lens on Microbial Assembly and Function Gut microbiota contribute fundamentally to host metabolism, immune calibration, nutrient processing, and epithelial barrier integrity. These functions are not only shaped by microbial taxonomic composition but also by emergent properties of community structure and assembly. A substantial body of work demonstrates that within-sample diversity (α-diversity) correlates with microbial richness, metabolic flexibility, and functional potential. However, a growing body of theory and empirical evidence increasingly recognizes that variation between communities—β-diversity—is an equally critical determinant of system-level resilience, responsiveness, and ecological function (Mori, Isbell, and Seidl (2018)). Moreover, [it is a more?] biologically-meaningful dimension of microbiome [variation]…

In both natural ecosystems and host-associated microbiotas, β-diversity captures how communities differ—in composition, structure, and inferred function—across space, time, or treatment. This inter-sample variation plays a central role in shaping not only which functions are present, but how they are distributed, and whether they are redundant or complementary across systems. In the context of complex diseases such as non-alcoholic fatty liver disease (NAFLD), it is increasingly evident that microbial community composition varies substantially between individuals even under identical genetic and dietary backgrounds, and that this variation may modulate host susceptibility to metabolic dysfunction.

Community dissimilarity, often quantified using Bray–Curtis or Sørensen metrics, reflects the cumulative effects of compositional turnover, richness differences, and abundance shifts among samples. Partitioning these dissimilarities into ecologically interpretable components enables inference about whether observed changes reflect species replacement (turnover), nested loss of taxa, or shifts in relative abundance/dominance hierarchies. This approach provides a framework for understanding the ecological processes underlying microbial community assembly, including deterministic filtering, stochastic drift, and priority effects, and for interpreting how these processes contribute to microbial restructuring under dietary perturbation.

Ecological theory frames β-diversity as the product of community assembly mechanisms, including deterministic filtering (e.g., diet, inflammation), stochastic drift, dispersal limitation, and priority effects. Distinguishing between these processes is particularly critical in microbiome research, where high inter-individual variability is often mischaracterized as “noise,” rather than the output of structured, ecological rules.

By decomposing β-diversity into interpretable ecological components—such as turnover vs. nestedness for presence–absence data, or balanced variation vs. abundance gradients for count-based measures—we can infer whether microbial community differences reflect species replacement, richness loss, or shifts in dominance hierarchies (Mori, Isbell, and Seidl (2018)). In the context of NAFLD, where diet is both a modifier of microbial composition and a driver of hepatic injury, these distinctions can help link microbial change to functional outcomes.

This approach provides a direct, process-based means of interrogating community assembly mechanisms. For instance, increasing turnover within or between groups may signal deterministic filtering (e.g., by diet), while reduced dissimilarity across timepoints may reflect convergence or biotic homogenization. Applying this framework to host-associated microbial communities allows us to evaluate how community divergence unfolds across time or under intervention, and whether this restructuring aligns with disease trajectories or recovery.

Community-Level Variation and Functional Consequences Recent advances in biodiversity–ecosystem function (BEF) theory emphasize that no single microbial community can support all ecosystem or host functions simultaneously. Rather, functional multifunctionality—the simultaneous maintenance of multiple beneficial functions—requires heterogeneous communities, each supporting different but complementary functional capacities. This model implies that high β-diversity, particularly across individuals exposed to different perturbations (e.g., high-fat diets), is essential for maintaining system-level functional capacity.

In host-associated systems, the relevance of β-diversity becomes particularly acute under perturbations such as dietary shifts, which act as strong environmental filters. Under these conditions, community convergence (low β-diversity) may signal functional homogenization, potentially constraining host adaptation, resilience, or therapeutic responsiveness. Conversely, sustained or increasing β-diversity may reflect adaptive diversification or ecological succession, potentially supporting functional insurance in the face of metabolic stress.

Notably, Mori, Isbell, and Seidl (2018) argue that β-diversity not only reflects variation in function, but also links the causes and consequences of biodiversity change by embedding diversity patterns within the processes of community assembly and the provisioning of ecosystem services. This framework is particularly compelling in the gut–liver axis, where microbial metabolism directly influences lipid accumulation, inflammation, and fibrosis—hallmarks of NAFLD progression.

[alt: A growing body of ecological theory argues that no single microbial community can support all host functions at maximal efficiency, particularly in complex or perturbed systems. Instead, functional complementarity across communities—enabled by taxonomic variation and niche partitioning—is required to maintain broader system-level resilience and functional capacity. From this perspective, inter-individual variation in microbiome composition (i.e., high community dissimilarity) may be functionally beneficial, particularly when paired with overlapping or redundant core functions.

In contrast, community convergence—for instance, under the influence of a high-fat diet—may constrain the range of available microbial functions, reduce redundancy, and increase susceptibility to perturbation. These concepts are well-established in environmental microbiology and are now being applied to host-associated systems, where microbial community structure is increasingly understood as both a consequence and a driver of host phenotype. In NAFLD, this may be particularly relevant, as early microbial shifts may precede or amplify downstream metabolic or immunologic dysfunction. ]

Aims In the context of HFD-induced NAFLD, we hypothesize that diet cohorts will diverge compositionally over time via deterministic filtering, and that such divergence can be parsed into distinct ecological components (species turnover, nestedness, abundance gradients). These patterns may predict microbiome-driven variation in HOST PHENOTYPE.

In this study, we investigate how gut microbial communities respond to long-term dietary intervention in a murine model of NAFLD [+ HFD-LA + knockout mice]. We collected weekly stool samples across a 12-week time series, encompassing a shared initial community (Week 0) and divergent dietary exposures thereafter. All mice began with the same baseline control-diet, then transitioned to one of four diets: a standard control or one of three high-fat diets differing in composition or host background. Stool samples were collected weekly for 12 weeks, enabling temporal profiling of microbial dynamics.

To interrogate patterns of microbial divergence, we applied ecological partitioning of β-diversity, using both presence/absence-based (Sorensen) and abundance-based (Bray-Curtis) frameworks. By decomposing these into turnover vs. nestedness and balanced variation vs. gradient components, respectively, we aim to address the following questions:

We applied both presence–absence and abundance-based dissimilarity metrics to quantify divergence in microbial communities over time. By decomposing microbial community dissimilarity into interpretable and ecologically meaningful components: turnover and nestedness for presence/absence (Sørensen) and balanced and gradient components for abundance (Bray–Curtis), we aimed to address the following questions:

- To what extent do microbial communities diverge over time within and between diet cohorts?

- Are these changes driven predominantly by species replacement, loss of richness, or abundance shifts?

- Do high-fat diets induce deterministic filtering and convergence across replicates, or preserve individualized community trajectories?

- Can these patterns of community dissimilarity inform future efforts to modulate the microbiome in NAFLD?

[alt: Aims: Determine how microbial communities diverge over time and across distinct high-fat and control diets using β-diversity partitioning.

Link microbial community assembly processes (e.g., deterministic filtering vs. stochastic drift) to specific dietary interventions and host metabolic states.

Evaluate temporal dynamics of β-diversity (turnover, nestedness, Bray partitions) to infer successional patterns or biotic homogenization associated with NAFLD development.

Ultimately, connect microbial compositional divergence to functional potential and host phenotype trajectories (e.g., liver histology, metabolic markers, inflammation).]

This approach enables us to interpret compositional change not merely as a shift in taxa, but as the emergent outcome of ecological processes. By integrating ecological theory with longitudinal microbiome profiling, this study provides a [mechanistic] foundation for understanding how diet-induced microbial restructuring unfolds at the community level—and how these changes may contribute to metabolic disease progression or resilience.

3 Results


We first used a community ecology framework to assess … Our [analytical aims] were 1) characterize the baseline community variation … 2) assess temporal trajecories within and among diet cohorts 3) build off the previous to narrow in on key HFD-driven, temporally-relevant, perburations in community structure. By partitioning community dissimilarity [… explain betapart rationale] … Following Mori, Isbell, and Seidl (2018), we treat β-diversity not just as a pattern, but as a mechanistic lens to infer how microbial community assembly processes shape functional outcomes under …diets… over time.”

3.1 Characterizing Day 0 Communities


3.1.1 Compositional overlaps

the baseline compositional heterogeneity (or lack thereof) among samples that began with identical conditions.

This is important because: - It defines how much divergence we expect by chance or stochasticity - allows us to say later, “This diet/timepoint exceeded initial inter-individual variability” - It gives us a framework to talk about assembly (even at T0)

Families Turicibacteraceae and Rikenellaceae and genera Alistipes, Duncaniella (G-), Limosilactobacillus, and Turicibacter were top 10 for Week-0, but not whole dataset. Conversely, Bacteroidaceae and Bifidobacteriaceae were not in the top 10 families for Week-O, nor were Bifidobacterium, Faecalibaculumm, uncl_Erysipelotrichaceae, or uncl_Oscillospiraceae in the top 10 genera for Week-O.

  • About Duncaniella: - “We performed a large-scale experiment using 579 genetically identical laboratory mice from a single animal facility, designed to identify the causes of disease variability in the widely used dextran sulphate sodium mouse model of inflammatory bowel disease. Commonly used treatment endpoint measures—weight loss and intestinal pathology—showed limited correlation and varied across mouse lineages. Analysis of the gut microbiome, coupled with machine learning and targeted anaerobic culturing, identified and isolated two previously undescribed species, Duncaniella muricolitica and Alistipes okayasuensis, and demonstrated that they exert dominant effects in the dextran sulphate sodium model leading to variable treatment endpoint measures. We show that the identified gut microbial species are common, but not ubiquitous, in mouse facilities around the world, and suggest that researchers monitor for these species to provide experimental design opportunities for improved mouse models of human intestinal diseases.” (Forster et al. (2022))



3.1.2 Dissimilarity Partitioning

Sections goals: - “What is the magnitude and nature of baseline variation?” Understand the baseline heterogeneity among starting microbiomes before dietary divergence. - Identify range of dissimilarity, dominant mechanisms (turnover vs nestedness). - Find any anomalous pairwise outliers. - Establish a baseline null model for future divergence comparisons.

The initial/starting communities (n = 5) contained over half of all of the OTUs detected across the whole dataset (364 of the total 624 OTUs, or 58.3%). Each of the 5 random Week-0 stool samples were comprised of 301 to 311 OTUs. These communities had an overall abundance-based multiple-site dissimilarity of 0.175 (betapart::beta.multi.abund()) and a presence/absence-based total multiple-site dissimilarity of 0.204 (betapart::beta.multi()). The turnover or species replacement component, measured as the Simpson dissimilarity, represented 93.99% of the total presence/absence-based dissimilarity. Pairwise comparisons of Week-0 communities showed that a minimum 23 of and a maximum of 30 OTUs were not shared between pairs of these initial samples.

The low dissimilarity values of Week-0 samples (abundance-based multiple-site dissimilarity of 0.175 (betapart::beta.multi.abund()) and a presence/absence-based total multiple-site dissimilarity of 0.204) indicates that the baseline communities were relatively homogeneous. Closer examination of these differences revealed that the vast majority, 93.99%, of dissimilarity was attributable to turnover, suggesting that even at baseline, species replacement — not simple richness differences — was the primary mechanism differentiating these microbiomes. This level of heterogeneity at T0 provides a critical context: downstream changes due to diet must exceed this baseline variability to be considered biologically meaningful. This Week-0 landscape acts as a null model against which future community divergence (due to diet and time) can be compared, and should be factored into interpretation of assembly trajectories.

3.1.3 Proportions of Rare vs. Dominant Taxa

Goals here: 1. Show [macro-level] microbial community structure shifts under HFD.

2. Highlight how dominant vs. low-abundance taxa change across groups (e.g., HFD vs. control).

3. Establish whether certain taxonomic tiers (e.g., >10%) become more/less dominant.

4. Lay groundwork for linking those shifts to intestinal barrier defects or downstream disease.\



3.1.4 Figure 1 - Characterizing Day 0 Communities

Figure X. Community structure and compositional variability among Week-0 microbiotas. (A) Family-level taxonomic profiles are relatively consistent across five pre-intervention stool samples, with communities dominated by Lactobacillaceae, Muribaculaceae, unclassified Bacteroidales, Lachnospiraceae (all families with a >10% mean relative abundance).(B - old) Observed richness ranged from 301 to 311 OTUs (mean = 304.2), and Shannon entropy ranged from 4.31 to 4.39 (mean = 4.35), indicating modest baseline heterogeneity. (C) Pairwise Bray–Curtis dissimilarities ranged from 0.06 to 0.08 (mean = 0.07), defining the magnitude of inter-individual variation at baseline. (D) Distance to group centroid (mean = 0.044) quantifies beta dispersion under shared, pre-intervention conditions. These data define a reference distribution of compositional variability (/baseline variablility) that contextualizes subsequent changes under dietary exposure.

Figure 3.1: Figure X. Community structure and compositional variability among Week-0 microbiotas. (A) Family-level taxonomic profiles are relatively consistent across five pre-intervention stool samples, with communities dominated by Lactobacillaceae, Muribaculaceae, unclassified Bacteroidales, Lachnospiraceae (all families with a >10% mean relative abundance).(B - old) Observed richness ranged from 301 to 311 OTUs (mean = 304.2), and Shannon entropy ranged from 4.31 to 4.39 (mean = 4.35), indicating modest baseline heterogeneity. (C) Pairwise Bray–Curtis dissimilarities ranged from 0.06 to 0.08 (mean = 0.07), defining the magnitude of inter-individual variation at baseline. (D) Distance to group centroid (mean = 0.044) quantifies beta dispersion under shared, pre-intervention conditions. These data define a reference distribution of compositional variability (/baseline variablility) that contextualizes subsequent changes under dietary exposure.


3.2 Temporal Beta Diversity Decomposition: Within vs. Between Timepoints & Diets (How communities change over time)


Sections goals: - Quantify how beta diversity components (Sørensen: turnover/nestedness, Bray: balanced/gradient) evolve within each cohort across time. - Show temporal stabilization, dietary divergence, and succession mechanisms.

plots are in SI…

3.2.1 Within-timepoint (community variation by week x diet)

3.2.1.1 Venn diagrams by diet by week










3.2.2 Diet-driven change over time




3.3 Questions in-progress

3.3.1 Whether different diets impose distinct assembly trajectories

3.3.2 What mechanisms (turnover vs nestedness, abundance shifts) drive those changes

3.3.3 When these changes stabilize or diverge

3.3.4 [Find G- taxa that consistently increase in HFD (for potential taxa-specific LPS characterization later)]

3.3.5 Correlations with host phenotype data

Major indicators of disease state and the relevant time periods

Description of host phenotype markers. Data include host disease state and proxy measurements by diet and timepoint. (These are just my notes — please suggest improvements)
Quantitative indicators of steatosis/steatohepatitis/fibrosis/cirrhosis Disease (Proxy) Measurements Control-diet HFD HFD-LA Villin-Cre-HFD
Overview/Notes Generally, increased IP by week 4 and disease state by week 6 No leaky gut phenotype ?
Indicator of barrier dysfunction Serum LPS (in healthy mice: 50-100 pg/mL) - Large molecule, needs defective barrier to cross into bloodstream (via portal vein -> then liver disease) Based on linear standard curve - Big jumps in weeks 3 & 4, then decreases by still high
Luminal LPS?
Dextran 4kd Flux Starts to increase vs control in week 3
Dextran 10kd Flux only week 12
Defective liver function serum ALT
? Percentage of weight gain throughout trial

Alterations in intestinal permeability and related markers

  • findings related to intestinal permeability measurements, including serum (and luminal + fecal?) LPS levels - Generally, week 4 saw increase in IP - Generally, week 6 saw disease state - LA treatments saw no leaky gut

Expression and activity of MLCK

  • MLCK mRNA expression, protein levels, and kinase activity (up-regulated)
  • I don’t have these data…

Activation of inflammatory pathways:

  • enterocyte TLR4/CD14 expression, IκBα phosphorylation, and NF-κB translocation (evidence of immune cell infiltration)
  • I don’t have these data…


4 Additional figures (likely SI)





6 Discussion

[Interpret how HFD-induced microbiome changes contribute to increased intestinal permeability and subsequent NAFLD development, integrating existing literature.]

HFDs Reshape Microbial Communities via Deterministic Assembly/β-Diversity Reflects Compositional and Functional Divergence Across treatment cohorts and timepoints, we interpret β-diversity as a quantitative summary of divergence in community structure. The partitioning of dissimilarity into turnover vs. nestedness (Sorensen) and balanced vs. gradient components (Bray) enables us to distinguish between taxonomic replacement, richness loss, and abundance shifts as underlying drivers of divergence.

The observed increase in turnover among HFD-fed mice may reflect strong environmental filtering driven by diet composition or host inflammatory state. These findings are consistent with deterministic models of community assembly and suggest that HFD imposes reproducible selective pressures on gut ecosystems.

Loss of β-Diversity as a Marker of Microbial Homogenization in NAFLD/Diet and Time Structure Community Assembly Trajectories Temporal and treatment-specific patterns in β-diversity can be mapped onto ecological assembly mechanisms. For example, increasing turnover over time within a diet cohort would be consistent with deterministic filtering, while low or declining turnover across diets may reflect biotic homogenization, a pattern associated with functional redundancy loss and resilience decline.

If community divergence decreases across HFD replicates over time, this could signal biotic homogenization—a pattern previously linked to loss of ecosystem resilience. In the NAFLD context, this may indicate a constrained microbial landscape with reduced capacity for functional compensation or recovery.

Temporal β-Trajectories Reflect Successional Dynamics in Diet-Structured Microbiomes/β-Diversity as a Predictor of Functional Potential While α-diversity provides information about richness at the sample level, β-diversity provides insight into the distribution of functional potential across individuals or environments. Distinct microbial configurations among replicates or cohorts may reflect complementary metabolic or immunological roles, especially relevant in the context of host–microbiome interactions.

The rise or fall of Bray-Curtis turnover or gradient components over time may mark distinct successional regimes under dietary stress. These dynamics may correspond to early instability followed by compositional stabilization—whether toward a dysbiotic state or a new equilibrium remains to be determined.

Implications for Microbiome Modulation and Therapeutics If dietary intervention induces convergence (i.e., reduced β-diversity), this may suggest loss of ecological insurance or constraint of functional redundancy. Conversely, sustained compositional heterogeneity may reflect resilience or adaptation. Understanding how β-diversity structures functional output has clear implications for microbiome engineering and translational strategies.

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References

Forster, Samuel C, Simon Clare, Benjamin S Beresford-Jones, Katherine Harcourt, George Notley, Mark D Stares, Nitin Kumar, et al. 2022. “Identification of Gut Microbial Species Linked with Disease Variability in a Widely Used Mouse Model of Colitis.” Nature Microbiology 7 (4): 590–99. https://doi.org/10.1038/s41564-022-01094-z.
Mori, Akira S., Forest Isbell, and Rupert Seidl. 2018. “Β-Diversity, Community Assembly, and Ecosystem Functioning.” Trends in Ecology & Evolution 33 (7): 549–64. https://doi.org/10.1016/j.tree.2018.04.012.